A DRL policy learns racing controls from depth spectral distributions using a non-geometric physics-informed reward, achieving 12% better performance than humans on out-of-distribution tracks with under 1% of baseline computation.
Pybullet, a python module for physics simulation for games, robotics and machine learning
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A neural model reduces high-resolution tactile elastomer simulation cost by over 65% while improving geometric fidelity and enabling differentiable inference.
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Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing
A DRL policy learns racing controls from depth spectral distributions using a non-geometric physics-informed reward, achieving 12% better performance than humans on out-of-distribution tracks with under 1% of baseline computation.
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Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
A neural model reduces high-resolution tactile elastomer simulation cost by over 65% while improving geometric fidelity and enabling differentiable inference.